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A framework for usage pattern–based power optimization and battery lifetime prediction in smartphones

  • Nirmal PandeyEmail author
  • Om Prakash Verma
  • Amioy Kumar
Original Article

Abstract

The use of mobile devices has increased many folds over the last few years. Smart phones are used not only for communication but also for storage of personal contents like photos, videos, documents, and bank account and credit/debit card details. Secure access to these contents is very important. Nowadays, many mobile devices come with inbuilt biometric-enabled security features like fingerprint, face recognition, and iris. However, additional battery power is consumed each time a user unlocks the device using any one of these security features. In order to enable prolonged use of the device, there is a strong need to find ways to conserve power in mobile devices. At the same time, it is also equally important that the smart phone user knows how long the battery of his device will last. In this paper, we present a novel power optimization and battery lifetime prediction framework called P4O (Pattern, Profiling, Prediction, and Power Optimization). Our contributions are threefold—(i) Propose a novel framework for power optimization in smart phones. (ii) Propose a new approach for battery lifetime forecast. (iii) Implement and validate the efficacy of the proposed framework. For experimental results, the proposed framework was implemented on the Android-based smartphone. The experimental results validate the proposed framework with power optimization up to 40% over default Linux and Android power saving features available in an Android operating system. This framework is also able to forecast battery lifetime with accuracy of up to 98%.

Keywords

Energy efficiency Mobile devices Battery lifetime Usage pattern Power optimization 

Notes

References

  1. 1.
    Gartner Report, Egham, UK, February 15, 2017. Available at: http://www.gartner.com/newsroom/id/3609817. Accessed 27 March 2019
  2. 2.
  3. 3.
    Samsung Galaxy Note 7 with inbuilt Iris scanner. https://news.samsung.com/global/everything-you-need-to-know-about-the-galaxy-note7s-iris-scanner. Accessed 27 March 2019
  4. 4.
    Samsung Galaxy S7 Edge specifications. https://www.techradar.com/reviews/phones/mobile-phones/samsung-galaxy-s7-edge-1315189/review. Accessed 27 March 2019
  5. 5.
    IObit Applock face recognition unlock Android application. http://www.iobit.com/en/applock.php. Accessed 27 March 2019
  6. 6.
    Phone user behaviour report for unlocking phones. http://www.phonearena.com/news/Did-you-know-people-unlock-their-phones-an-average-100-times-a-day_id74189. Accessed 27 March 2019
  7. 7.
    Rahmati A, Qian A, Zhong L (2007) Understanding human-battery interaction on mobile phones. In: Proceedings of the 9th international conference on human computer interaction with mobile devices and services. ACM, SingaporeGoogle Scholar
  8. 8.
    Balani R (2007) Energy consumption analysis for Bluetooth, Wi-Fi and cellular networks. White Paper, Tech RepublicGoogle Scholar
  9. 9.
    Rice A, Hay S (2010) Decomposing power measurements for mobile devices. In: IEEE International Conference on Pervasive Computing and Communications. Manheim, Germany 29 March – 2 April 2010Google Scholar
  10. 10.
    Carroll A (2010) An analysis of power consumption in a smart-phone. In: Proc. USENIX Annual Technical Conference. Boston, MA, USA, 23–25 June 2010Google Scholar
  11. 11.
    Perrucci GP, Fitzek FHP, Widmer J (2011) Survey on energy consumption entities on the smartphone platform. In: 73rd IEEE Vehicular Technology Conference (VTC). Yokohoma, Japan, 1–5 May 2011Google Scholar
  12. 12.
    Paul K, Kundu TK (2010) Android on mobile devices: an energy perspective. In: 10th IEEE International Conference on Computer and Information Technology (CIT). Bradford, UK, 29 June – 1 July 2010Google Scholar
  13. 13.
    Lyons K, Hightower J, Huang EM (eds) (2011) Understanding human-smartphone concerns: a study of battery life. Pervasive 2011, LNCS 6696. Springer-Verlag, Berlin Heidelberg, pp 19–33Google Scholar
  14. 14.
    Demumieux R, Losquin P (2005) Gather customer’s real usage on mobile phones. In: Proceedings of the 7th international conference on Human computer interaction with mobile devices & services. ACM, pp 267–270Google Scholar
  15. 15.
    Kang JM, Seo S, Hong J (2011) Usage pattern analysis of smartphones. In: 13th Asia-Pacific Network Operations and Management Symposium. pp 1–8Google Scholar
  16. 16.
    Oliver E (2010) The challenges in large-scale smartphone user studies. In: Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement. ACM, pp 1–5Google Scholar
  17. 17.
    Lee J, Joe H, Kim H (2012) Smart phone power model generation using use pattern analysis. In: IEEE International Conference on Consumer Electronics. pp 412–413Google Scholar
  18. 18.
    Falaki H, Lymberopoulos D, Mahajan R, Govindan R, Kandula S, Estrin D (2010) Diversity in smartphone usage. In: Proc. ACM MOBISYSGoogle Scholar
  19. 19.
    Verkasalo H (2010) Analysis of smartphone user behavior. In: Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), Ninth International Conference on IEEE, 2010, pp 258–263Google Scholar
  20. 20.
    Oulasvirta A, Rattenbury T, Ma L, Raita E (2012) Habits make smartphone use more pervasive. In Pers Ubiquit Comput 16:105–114CrossRefGoogle Scholar
  21. 21.
    Adrakatti AF, Mulla KR (2017) A realistic approach to information services on mobile apps. J Access Serv 14(1)Google Scholar
  22. 22.
    Min AW, Wang R, Tsai J, Ergin MA, Tai TYC (2012) Improving energy efficiency for mobile platforms by exploiting low-power sleep states. CF’12, may 15–17, Cagliari, ItalyGoogle Scholar
  23. 23.
    Al-Turjman F (2017) Cognitive-node architecture and a deployment strategy for the future sensor networks. Mobile Networks and Applications, Springer, ISBN 9781138102293 - CAT# K34952.  https://doi.org/10.1007/s11036-017-0891-0
  24. 24.
    Singh GT, Al-Turjman FM (2016) Learning data delivery paths in QoI-aware information-centric sensor networks. IEEE Internet Things J 3(4):572–580CrossRefGoogle Scholar
  25. 25.
    Kang J-M, Seo S-s, Hong JW-K (2011) Personalized battery lifetime prediction for mobile devices based on usage patterns. J Comput Sci Eng 5(4):338–345CrossRefGoogle Scholar
  26. 26.
    Krintz C, Wen Y, Wolski R (2004) Application-level prediction of battery dissipation. In: Proceedings of the International Symposium on Lower Power Electronics and Design. Newport Beach, CA, pp 224–229Google Scholar
  27. 27.
    Zhang L, Tiwana B, Dick RP, Qian Z, Mao ZM, Wang Z, Yang L (2010) Accurate online power estimation and automatic battery behaviour based power model generation for smartphones. In: Proceedings of the 8th IEEE/ACM International Conference on Hardware/Software-Co-Design and System Synthesis. Scottsdale, AZ, pp 105–114Google Scholar
  28. 28.
    Ferdous R, Osmani V, Mayora O (2015) Smartphone app usage as a predictor of perceived stress levels at workplace. In: 9th IEEE International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health). Istanbul, TurkeyGoogle Scholar
  29. 29.
    Al-Turjman F, Betin-Can A, Ever E, Alturjman S (2016) Ubiquitous cloud-based monitoring via a mobile app in smartphones: an overview. In: IEEE International Conference on Smart Cloud (SmartCloud). New York, NY, USAGoogle Scholar
  30. 30.
    Elgedawy I, Al-Turjman F (2016) IdProF: identity provisioning framework for smart environments. In: IEEE Conference HONET-ICT. Nicosia, CyprusGoogle Scholar
  31. 31.
    Li H, Lu X, Liu X, Xie T, Bian K, Lin FX, Feng, F (2015) Characterizing smartphone usage patterns from millions of Android users. In: Proceedings of the 2015 ACM Conference on Internet Measurement Conference, pp 459–472Google Scholar
  32. 32.
    Lu EHC, Lin YW, Ciou JB (2014) Mining mobile application sequential patterns for usage prediction. In: 2014 IEEE International Conference on Granular Computing (GrC). Noboribetsu, Japan, pp 185–190Google Scholar
  33. 33.
    Zipf GK (1965) Human behaviour and the principle of least effort: an introduction to human ecology. Hafner Publishing Co., New YorkGoogle Scholar
  34. 34.
    Google Android developers information. Available at: https://developer.android.com/index.html. Accessed 27 March 2019
  35. 35.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45CrossRefGoogle Scholar
  36. 36.
    Monsoon Power Monitor by Monsoon Solutions Inc. Available at: https://www.msoon.com/LabEquipment/PowerMonitor/. Accessed 27 March 2019
  37. 37.
    Samsung Galaxy J2 (2016) Available at: http://www.samsung.com/in/smartphones/galaxy-j2-2016-j210f/SM-J210FZDDINS/. Accessed 27 March 2019

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Delhi Technological University (DTU)DelhiIndia

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